摘要
数字预失真是改善功率放大器非线性化比较有效的技术方法。文章针对射频功率放大器的非线性强记忆效应带来的失真问题,在分数阶记忆多项式模型和传统双线性多项式模型的基础上,提出了一种改进的分数阶双线性多项式数字预失真模型。该模型利用间接学习结构并结合递推最小二乘(RLS)算法对建立的预失真模型进行自适应预失真系统仿真。仿真结果表明,该模型与传统的双线性多项式模型相比,归一化均方误差(NMSE)提高了2.6 dB,系数减少了34.3%,邻道功率泄漏比(ACPR)改善了约5 dB,由此可知,该模型能够有效地补偿功率放大器的非线性失真。
Digital predistortion is an effective technical method to improve the nonlinearization of power amplifier. Based on the fractional-order memory polynomial model and the traditional bilinear polynomial model, an improved fractional-order bilinear polynomial model predistortion method for indirect learning structure is proposed. The model uses the indirect learning structure and Recursive Least Square(RLS) adaptive identification algorithm to simulate the adaptive predistortion system. The experimental results show that, compared with the traditional bilinear polynomial model, the Normalized Mean Square Error(NMSE) is improved by 2.6 dB with 34.3% reduction of the number of model coefficients and the improvement of Adjacent Channel Power Ratio(ACPR) of 5 dB. Therefore, the model can effectively compensate the distortion of the power amplifier.
作者
王恒
徐方雨
WANG Heng;XU Fang-yu(School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065,China)
出处
《光通信研究》
北大核心
2020年第2期44-49,共6页
Study on Optical Communications
基金
国家科技重大专项基金资助项目(2018ZX03001007-004)。
关键词
数字预失真
记忆多项式
间接学习结构
功率放大器
digital predistortion
memory polynomial
indirect learning architecture
power amplifiers
